Assoc.Prof. Mu-Yen Chen
National Cheng Kung University
Speech title：Using deep learning models to detect fake news about COVID-19
The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of COVID-19, a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the World Health Organization (WHO), posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation.
This speech will discuss the multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selections. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model can be selected for fake news detection. In this speech, the experimental design and performance evaluation will be discussed in details and the findings can be a reference for the future research.
Dr. Mu-Yen Chen is working as an Associate Professor at National Cheng Kung University, Taiwan. Previously, he was a Professor of Information Management at the National Taichung University of Science and Technology, Taiwan. He received his Ph.D. in Information Management from National Chiao-Tung University in Taiwan. His current research interests include artificial intelligence, soft computing, bio-inspired computing, data mining, deep learning, context-awareness, and machine learning, with more than 100 publications in prestigious venues such as IEEE Transactions on Fuzzy Systems, IEEE IoT, IEEE Sensors, IEEE Access, ACM Transactions on Internet Technology, Applied Soft Computing, Soft Computing, Neurocomputing, Computer Networks, and FGCS. He has served as Editor in Chief on International Journal of Big Data and Analytics in Healthcare, and Associate Editor of IEEE Access, Applied Soft Computing, Granular Computing, Human-centric Computing, and Information Sciences, Journal of Medical and Biological Engineering, International Journal of Social and Humanistic Computing and Journal of Information Science and Engineering while he is an editorial board member on several SCI journals.